Recent methods for boundary or edge detection built on Deep Convolutional Neural Networks (CNNs) typically suffer from the issue of predicted edges being thick and need post-processing to obtain crisp boundaries. Highly imbalanced categories of boundary versus background in training data is one of main reasons for the above problem. In this work, the aim is to make CNNs produce sharp boundaries without post-processing. We introduce a novel loss for boundary detection, which is very effective for classifying imbalanced data and allows CNNs to produce crisp boundaries. Moreover, we propose an end-to-end network which adopts the bottom-up/top-down architecture to tackle the task. The proposed network effectively leverages hierarchical features and produces pixel-accurate boundary mask, which is critical to reconstruct the edge map. Our experiments illustrate that directly making crisp prediction not only promotes the visual results of CNNs, but also achieves better results against the state-of-the-art on the BSDS500 dataset (ODS F-score of .815) and the NYU Depth dataset (ODS F-score of .762).

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An Aspergillus versicolor isolated from sediment collected from the Bohai Sea, China, yielded the new dimeric diketopiperazine brevianamide S (1), together with three new monomeric cometabolites, brevianamides T (2), U (3), and V (4). Structures were determined by detailed spectroscopic analysis. Brevianamide S exhibited selective antibacterial activity against Bacille Calmette-Guérin (BCG), suggestive of a new mechanism of action that could inform the development of next-generation antitubercular drugs.

Selective removal of organic pollutants by advanced oxidation methods has been receiving increasing attention for environmental remediation. In this study, a novel catalyst, which can selectively oxidize phenolic compounds (PCs) based on their hydrophobicity, composed of metal–organic-framework-derived Fe/Fe3O4 and three-dimensional reduced graphene oxide (rGOF) is designed for peroxydisulfate (PDS) activation. This heterogeneous PDS activation system can completely degrade hydrophobic PCs within 30 min. By investigating the hydrophobic properties of eight representative PCs, a positive correlation between the hydrophobicity of PC and the reaction kinetics is reported for the first time. The selective removal stems from the strong interaction between highly hydrophobic PCs and the catalyst. Moreover, the mechanism investigation shows that the degradation reaction is triggered by interface reactive oxygen species (ROS). Our study reveals that the selective degradation of organic pollutants by PDS activation depends on the hydrophilic and hydrophobic properties of the pollutant and catalyst. The reported results provide new insights into a highly selective and efficient PDS activation system for organic pollutant removal.

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Tripterygium wilfordii Hook F. (TwHF) based therapy has been proved as effective in treating rheumatoid arthritis (RA), yet the predictors to its response remains unclear. A two-stage trial was designed to identify and verify the baseline symptomatic predictors of this therapy. 167 patients with active RA were enrolled with a 24-week TwHF based therapy treatment and the symptomatic predictors were identified in an open trial; then in a randomized clinical trial (RCT) for verification, 218 RA patients were enrolled and classified into predictor positive (P+) and predictor negative (P−) group, and were randomly assigned to accept the TwHF based therapy and Methotrexate and Sulfasalazine combination therapy (M&S) for 24 weeks, respectively. Five predictors were identified (diuresis, excessive sweating, night sweats for positive; and yellow tongue-coating, thermalgia in the joints for negative). In the RCT, The ACR 20 responses were 82.61% in TwHF/P+ group, significantly higher than that in TwHF/P− group (P = 0.0001) and in M&S/P+ group (P < 0.05), but not higher than in M&S/P− group. Similar results were yielded in ACR 50 yet not in ACR 70 response. No significant differences were detected in safety profiles among groups. The identified predictors enable the TwHF based therapy more efficiently in treating RA subpopulations.

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